The need to identify and authenticate individuals is greater today than it has ever been, and is particularly acute for applications such as homeland security, law enforcement, electronic commerce, access control and privacy protection, to name a few.
The use of biometrics in general, and fingerprint recognition in particular, to identify and authenticate humans is a proven method that dates back several centuries. However, to perform identification and authentication in many of the applications envisaged today, speed is of the essence. Thus, there is a need for automated (computer-assisted) fingerprint recognition, where delays due to human intervention are kept to a minimum or eliminated altogether, but without sacrificing accuracy.
While popular television shows and movies portray computer-assisted fingerprint recognition as a seemingly perfected technology, it is known in the biometrics community that serious impediments remain in at least two areas.
Firstly, conventional algorithms used by computers to conclude whether or not there is a “match” between a subject fingerprint image and a reference fingerprint image tend to fail to reach the correct conclusion in cases where the subject fingerprint image exhibits an unstable pattern of minutiae. Such instability arises largely due to the necessity of having a thin liquid layer on top of the fingertip (and, notably, within the valleys of the epidermis of the fingertip) to provide continuity for light wave propagation in so-called dark field image acquisition. Because liquid molecules trapped inside the valleys during the impression-taking process move in a dynamic fashion (e.g., due to blood flow under the skin and fluctuation in the applied pressure), there will result a significant difference in the minutia pattern under machine detection from one impression to the next, for the same finger. This instability renders the matching score achievable by conventional fingerprint recognition techniques low in comparison with certain other biometric technologies, such as iris scan, for example.
Secondly, recognizing that many fingerprint images available today for comparison were taken at some time in the past, it will be appreciated that such images cannot realistically be re-captured and re-entered into a database. Thus, depending on the conditions under which such “legacy” fingerprint images were taken, they may not be in an ideal coordinate system for reliable machine comparison relative to a subject fingerprint image. This again leads to the instability issue mentioned above as well as other problems.
Thus, there is a need in the fingerprint recognition art for a technological solution that overcomes at least in part the aforesaid deficiencies.